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Abstract
Obstructive sleep apnea (OSA) is a condition of cyclic, periodic ob-struction (stenosis) of the upper respiratory tract. OSA could be associated with serious cardiovascular problems, such as hypertension, arrhythmias, hearth failure or peripheral vascular disease. Understanding the way of connection between OSA and cardiovascular diseases is important to choose proper treatment strategy. In this paper, we present a method for integrated measurements of biosignals for automatic OSA detection. The proposed method was implemented using a porta-ble device with the application of the Support Vector Machine (SVM) classifier. The specific objective of this work is to analyze the minimum set of features for the ECG signal that could produce acceptable classification results. Those features can be further expanded using other biosignals, measured by the portable SleAp device. Additionally, the influence of the body movements and positions on meas-urement results with SleAp system are presented. The proposed system could help to determine the influence of OSA on the state of the cardiovascular system.
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Details
- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Published in:
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Advances in Intelligent Systems and Computing
no. 300,
pages 179 - 192,
ISSN: 2194-5357 - Title of issue:
- W : Human-Computer Systems Interaction: Backgrounds and Applications 3 strony 179 - 192
- Language:
- English
- Publication year:
- 2014
- Bibliographic description:
- Przystup P., Bujnowski A., Poliński A., Rumiński J., Wtorek J.: Sleep Apnea Detection by Means of Analyzing Electrocardiographic Signal// W : Human-Computer Systems Interaction: Backgrounds and Applications 3/ : Springer International Publishing, 2014, s.179-192
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-319-08491-6_15
- Verified by:
- Gdańsk University of Technology
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